/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    NormalizedPolyKernel.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions.supportVector;

import weka.core.Instance;
import weka.core.Instances;

/**
 * <!-- globalinfo-start --> The normalized polynomial kernel.<br/>
 * K(x,y) = &lt;x,y&gt;/sqrt(&lt;x,x&gt;&lt;y,y&gt;) where &lt;x,y&gt; =
 * PolyKernel(x,y)
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 *  -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)
 * </pre>
 *
 * <pre>
 *  -C &lt;num&gt;
 *  The size of the cache (a prime number), 0 for full cache and 
 *  -1 to turn it off.
 *  (default: 250007)
 * </pre>
 * 
 * <pre>
 *  -E &lt;num&gt;
 *  The Exponent to use.
 *  (default: 1.0)
 * </pre>
 * 
 * <pre>
 *  -L
 *  Use lower-order terms.
 *  (default: no)
 * </pre>
 * 
 * <!-- options-end -->
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class NormalizedPolyKernel extends PolyKernel {

    /** for serialization */
    static final long serialVersionUID = 1248574185532130851L;

    /**
     * default constructor - does nothing
     */
    public NormalizedPolyKernel() {
        super();

        setExponent(2.0);
    }

    /**
     * Creates a new <code>NormalizedPolyKernel</code> instance.
     *
     * @param dataset    the training dataset used.
     * @param cacheSize  the size of the cache (a prime number)
     * @param exponent   the exponent to use
     * @param lowerOrder whether to use lower-order terms
     * @throws Exception if something goes wrong
     */
    public NormalizedPolyKernel(Instances dataset, int cacheSize, double exponent, boolean lowerOrder) throws Exception {

        super(dataset, cacheSize, exponent, lowerOrder);
    }

    /**
     * Returns a string describing the kernel
     * 
     * @return a description suitable for displaying in the explorer/experimenter
     *         gui
     */
    public String globalInfo() {
        return "The normalized polynomial kernel.\n" + "K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)";
    }

    /**
     * Computes the result of the kernel function for two instances. If id1 == -1,
     * eval use inst1 instead of an instance in the dataset. Redefines the eval
     * function of PolyKernel.
     *
     * @param id1   the index of the first instance in the dataset
     * @param id2   the index of the second instance in the dataset
     * @param inst1 the instance corresponding to id1 (used if id1 == -1)
     * @return the result of the kernel function
     * @throws Exception if something goes wrong
     */
    public double eval(int id1, int id2, Instance inst1) throws Exception {

        double div = Math.sqrt(super.eval(id1, id1, inst1) * ((m_keys != null) ? super.eval(id2, id2, m_data.instance(id2)) : super.eval(-1, -1, m_data.instance(id2))));

        if (div != 0) {
            return super.eval(id1, id2, inst1) / div;
        } else {
            return 0;
        }
    }

    /**
     * Sets the exponent value (must be different from 1.0).
     * 
     * @param value the exponent value
     */
    public void setExponent(double value) {
        if (value != 1.0)
            super.setExponent(value);
        else
            System.out.println("A linear kernel, i.e., Exponent=1, is not possible!");
    }

    /**
     * returns a string representation for the Kernel
     * 
     * @return a string representaiton of the kernel
     */
    public String toString() {
        String result;

        if (getUseLowerOrder())
            result = "Normalized Poly Kernel with lower order: K(x,y) = (<x,y>+1)^" + getExponent() + "/" + "((<x,x>+1)^" + getExponent() + "*" + "(<y,y>+1)^" + getExponent() + ")^(1/2)";
        else
            result = "Normalized Poly Kernel: K(x,y) = <x,y>^" + getExponent() + "/" + "(<x,x>^" + getExponent() + "*" + "<y,y>^" + getExponent() + ")^(1/2)";

        return result;
    }

}
